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This article presents the analysis and optimization method of equivalent impedances specifically for multichannel wireless power transfer (WPT) systems. By establishing the equivalent impedance model and the calculation scheme about output power and system efficiency for multichannel WPT system, the proposed analysis and optimization method is capable to determine the proper range of the load resistances in each channel thereby achieving an ideal transmission performance for multichannel WPT systems. Besides, the optimal selection of the load resistances is capable of offering the great power transmission passageway without cross interference between different channels. Moreover, the proper value of the load resistances can achieve certain power output while maintaining the system efficiency at a relatively high level. Both the simulation and experimental results are provided to demonstrate the feasibility and the performance of the proposed scheme.

During the movement of the mobile robot, except the common scenario, sometimes, the robot has to face the unexpected uncertainty in the environment. Traditional methods to address this problem generally use the task-specific method from the engineering perspective, lacking flexibility in the changing environment and being difficult to respond to the environmental challenges. Because of the function of the brain’s neuromodulatory system, human beings have ability to respond to the ever-changing environment quickly. To simulate the working mechanism of the human brain in responding to the unexpected uncertainty in the environment, this article presents a motivated developmental network (MDN) to offer a control configuration for an artificial agent to face the unexpected uncertainty in the environment, through introducing the acetylcholine/norepinephrine (ACh/NE) systems to the MDN. Taking the regulation role of ACh/NE in serotonin and dopamine (DA) into account, a novel learning rate for the hidden layer neurons of the MDN is proposed. Moreover, a novel composite mechanism is presented to decide the moving direction of the agent. Under the modulation of DA, serotonin, ACh, and NE, the agent can perform specific functions effectively, e.g., chase a target and elude the obstacle, especially the sudden obstacle. Goal-directed pursuing behavior in three simulation cases illustrates the effect of the presented neural modulatory systems, for instance, dealing with the unexpected uncertainty, realizing the attentional effort, reinforcement learning, etc. To the best of our knowledge, this article is the first endeavor to address the unexpected uncertainty with the MDN.

Advanced ceramic sponge materials with temperature-invariant high compressibility are urgently needed as thermal insulators, energy absorbers, catalyst carriers, and high temperature air filters. However, the application of ceramic sponge materials is severely limited due to their complex preparation process. Here, we present a facile method for large-scale fabrication of highly compressible, temperature resistant SiO2-Al2O3 composite ceramic sponges by blow spinning and subsequent calcination. We successfully produce anisotropic lamellar ceramic sponges with numerous stacked microfiber layers and density as low as 10 mg cm−3. The anisotropic lamellar ceramic sponges exhibit high compression fatigue resistance, strain-independent zero Poisson’s ratio, robust fire resistance, temperature-invariant compression resilience from −196 to 1000 °C, and excellent thermal insulation with a thermal conductivity as low as 0.034 W m−1 K−1. In addition, the lamellar structure also endows the ceramic sponges with excellent sound absorption properties, representing a promising alternative to existing thermal insulation and acoustic absorption materials., Temperature-invariant highly compressible ceramic sponges are attractive for thermal insulators and energy absorbers, but development is limited by complex preparation processes. Here the authors report large-scale fabrication of silica-alumina composite ceramic sponges via blow spinning and calcination.

Autonomous navigation in dynamic environment is aprerequisite of the mobile robot to perform tasks, and numerous approaches have been presented, including the supervised learning. Using supervised learning in robot navigation might meet problems, such as inconsistent and noisy data, and high error in training data. Inspired by the advantages of the reinforcement learning, such as no need for desired outputs, many researchers have applied reinforcement learning to robot navigation. This paper presents anovel method to address the robot navigation in different settings, through integrating supervised learning and analogical reinforcement learning into amotivated developmental network. We focus on the effect of the new learning rate on the robot navigation behavior. Experimentally, we show that the effect of internal neurons on the learning rate allows the agent to approach the target and avoid the obstacle as compounding effects of sequential states in static, dynamic, and complex environments. Further, we compare the performance between the emergent developmental network system and asymbolic system, as well as other four reinforcement learning algorithms. These experiments indicate that the reinforcement learning is beneficial for developing desirable behaviors in this set of robot navigation- staying statistically close to its target and away from obstacle.

This paper investigates finite-time adaptive neural tracking control for a class of nonlinear time-delay systems subject to the actuator delay and full-state constraints. The difficulty is to consi...

Spiral wave is closely related to the occurrence of malignant ventricular arrhythmia. It is important and necessary to study the spiral wave dynamics to better analyze and control spiral waves. In this paper, the dynamics of FitzHugh-Nagumo(FHN) model is identified by using a novel method based on deterministic learning and interpolation method. The FHN model, which has been studied extensively in physical and mathematical science, is often used to study spiral waves. It is a distributed parameter (DPS) described by two coupled partial differential equations (PDEs). To identify the underlying system dynamics of the FHN model globally, we first transform the FHN model into a set of ordinary differential equations (ODEs) by applying the method of lines. Then, we identify the dynamics of the approximation system by employing deterministic learning. That is, the FHN dynamics on a set of spatial grid nodes is accurately identified. To achieve the global identification of the FHN model, the underlying system dynamics of the FHN model on any other spatial point is approximated via an algorithm based on the interpolation method. The effectiveness and feasibility of the proposed method are demonstrated theoretically and numerically.

Decentralized adaptive neural backstepping control scheme is developed for uncertain high-order stochastic nonlinear systems with unknown interconnected nonlinearity and output constraints. For the control of high-order nonlinear interconnected systems, it is assumed that nonlinear system functions are unknown. It is for the first time to control stochastic nonlinear high-order systems with output constraints. Firstly, by constructing barrier Lyapunov functions, output constraints are handled. Secondly, at each recursive step, only one adaptive parameter is updated to overcome over-parameterization problems, and RBF neural networks are used to identify unknown nonlinear functions so that the difficulties caused by completely unknown system functions and stochastic disturbances are tackled. Finally, based on the Lyapunov stability method, the decentralized adaptive control scheme via neural networks approximator is proposed, ultimately reducing the number of learning parameters. It is shown that the designed controller can guarantee all the signals of the resulting closed-loop system to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking errors for each subsystem are driven to a small neighborhood of zero. The simulation studies are performed to verify the effectiveness of the proposed control strategy.

This paper proposes an adaptive observer-based neural controller for a class of uncertain large-scale stochastic nonlinear systems with actuator delay and time-delay nonlinear interactions, where drift and diffusion terms contain all state variables of their own subsystem. First, a state observer is established for estimating the unmeasured states, and a predictor-like term is utilized to transform the input delayed system into the delay-free system. Second, novel appropriate Lyapunov–Krasovskii functionals are used to compensate the time-delay terms, and neural networks are employed to approximate unknown nonlinear functions. At last, an output-feedback adaptive neural control scheme is constructed by using Lyapunov stability theory and backstepping technique. It is shown that the designed neural controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error is driven to a small neighborhood of the origin. The simulation results are presented to further show the effectiveness of the proposed approach.

In the paper, we propose a novel approach to identify distributed parameter system (DPS) with recurrent state trajectory. Different from existing literature, the system dynamics rather than parameters or structure of DPS is identified in the study. Due to the infinite-dimensional feature of DPS, the partial differential equation describing the DPS is first approximated by a set of ordinary differential equations. By employing finite difference method, the spatial derivatives at a set of spatial points are approximated. Then, the DPS dynamics at the set of spatial points is identified via deterministic learning. With the identification results, a mechanism based on interpolation method is proposed to approximate the DPS dynamics at any other spatial point. That is, we can accurately identify the DPS dynamics at any spatial point. Numerical results involving the identification of an important mathematical physics equation are presented to illustrate the validity of the approach.

The problem of finite-time decentralized neural adaptive constrained control is studied for large-scale nonlinear time-delay systems in the non-affine form. The main features of the considered system are that 1) unknown unmatched time-delay interactions are considered, 2) the couplings among the nested subsystems are involved in uncertain nonlinear systems, 3) based on finite-time stability approach, asymmetric saturation actuators and output constraints are studied in large-scale systems. First, the smooth asymmetric saturation nonlinearity and barrier Lyapunov functions are used to achieve the input and output constraints. Second, the appropriately designed Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. Note that, due to unknown time-delay interactions and the couplings among subsystems, the controller design is more meaningful and challenging. At last, based on finite-time stability theory and Lyapunov stability theory, a decentralized adaptive controller is proposed, which decreases the number of learning parameters. It is shown that the designed controller can ensure that all closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin. The simulation studies are presented to show the effectiveness of the proposed method.

In this paper, the problem concerned with adaptive approximation-based control is discussed for a class of stochastic pure-feedback nonlinear time-delay systems with unknown direction control gains and full-state constraints. In the controller design process, the approximation capability of neural networks is utilized to identify the unknown nonlinearities, the appropriate Lyapunov–Krasovskii functionals are constructed to compensate the unknown time-delay terms, barrier Lyapunov functions (BLFs) are designed to ensure that the state variables are constrained, and the Nussbaum-type gain function is used to solve the difficulties caused by the unknown virtual control gains. Then, based on adaptive backstepping technique and Lyapunov stability theory, a robust control scheme is presented, and the developed controller decreases the number of learning parameters and thus reduces the computational burden. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a compact set of the origin. Finally, two simulation examples are included to validate the effectiveness of the proposed approach.

Ceramic ultra-thin film materials have attracted much attention because of their unique performance compared to the bulk materials and their vital applications in electronic devices and catalysts. An easy, effective and facile method to fabricate high quality ceramic nano membranes will significantly stimulate their practical applications. Herein, we report a new “Blow-bubble” process to produce large area freestanding ultra-thin ceramic material. We successfully used a blow-bubble method to obtain large area freestanding ultra-thin ZnO film after thermal process, and this method can also be used to other materials like TiO2. This synthesizing process relies on sol-gel method and surface tension. In summary, this work demonstrates a facile, economic and effective method to fabricate large area freestanding ultra-thin ZnO film. The photoconductivity of the ZnO film was also tested.

This paper investigates the problem of decentralized adaptive backstepping control for a class of large-scale stochastic nonlinear time-delay systems with asymmetric saturation actuators and output constraints. Firstly, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are designed to ensure that the output parameters are restricted. Secondly, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. At last, based on Lyapunov stability theory, a decentralized adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters. It is shown that the designed controller can ensure that all the closed-loop signals are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are provided to show the effectiveness of the proposed method.

This paper investigates an adaptive neural tracking control problem for a class of multi-input multi-output (MIMO) stochastic nonlinear pure-feedback systems with input saturation and full-state constraints. First, the implicit function and mean value theorems are used to overcome the difficulty in the control of pure-feedback systems. Second, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model, the neural network is employed to approximate the unknown nonlinearity, and a barrier Lyapunov function is designed to ensure that the full-state variables are restricted. At last, based on Lyapunov stability theory, a robust adaptive neural control method is obtained, which decreases the number of learning parameters and thus reduces the computational burden. It is shown that the designed controller can guarantee that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. The simulation example is given to further illustrate the effectiveness of the proposed approach.

Aiming at the aerodynamic optimization problem of guided ammunition airfoil, genetic algorithm is used to optimize the lift-drag ratio of the airfoil based CST geometric description method with given inflow velocity and fixed angle of attack. Firstly, the parameter space generated by CST parameterized airfoil description method is performed with binary code and the basic operations of genetic algorithm such as selection, crossover and mutation. Then, the external CFD solver is called to automatically divide the grid and calculate the lift-drag ratio through the control script. Finally, the target solution is obtained by searching. The calculation results show that this method can obtain a optimal target solution in relative less generation for a group of airfoils generated randomly in a certain search space.

This paper proposes an adaptive neural dynamic surface control scheme for a class of strict-feedback stochastic nonlinear systems with guaranteed predefined performance under arbitrary switchings. First, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured with unknown initial errors, and input constraints are achieved by employing a continuous differentiable asymmetric saturation model. Second, RBF neural networks are used to handle unknown nonlinear functions and stochastic disturbances, and the dynamic surface control technique is used to avoid the problem of ‘explosion of complexity’ in control design. At last, by combining the common Lyapunov function method with the backstepping design principle, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterization and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are four-moment (or two-moment) semi-globally uniformly ultimately bounded, and the prescribed transient and steady tracking control performance are guaranteed under arbitrary switchings. The arbitrary switching behaviors among two and three subsystems are performed to demonstrate and verify the effectiveness of the proposed method.

In this study, the adaptive tracking control is investigated for a class of stochastic pure-feedback non-linear time-delay systems with output constraint and asymmetric input saturation non-linearity. First, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model, and the barrier Lyapunov function is designed to cope with the output constraints. Then, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to address the effects of the unknown time-delay terms, and the neural network is employed to approximate the unknown non-linearities. At last, based on Lyapunov stability theory, a robust adaptive neural controller is proposed, which decreases the number of learning parameters and thus avoids the over-estimation problem. Under the designed neural controller, all the closed-loop signals are guaranteed to be 4-moment (or 2 moment) semi-globally uniformly ultimately bounded and the tracking error converges to a small neighbourhood of the origin for bounded initial conditions. Two simulation examples are presented to further illustrate the effectiveness of the designed method.

An adaptive neural tracking control is investigated for a class of nonstrict-feedback stochastic nonlinear time-delay systems with full-state constraints and saturation input. First, the continuous differentiable saturation model is employed to ensure the input constraint, and a barrier Lyapunov function is designed to achieve the full-state constraint. Second, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown time-delay terms, and neural networks are employed to approximate the unknown nonlinearities. Finally, based on Lyapunov stability theory, an adaptive controller is proposed to guarantee that all the signals in the closed-loop system are 4-Moment (or 2-Moment) semi-globally uniformly ultimately bounded and the tracking error converges to a small neighbourhood of the origin. Two examples are shown to further demonstrate the effectiveness of the proposed control scheme.

This paper studies an adaptive neural tracking control problem for a class of strict-feedback stochastic nonlinear systems with guaranteed predefined performance subject to unknown backlash-like hysteresis input. First, utilizing the prescribed performance control, the predefined tracking control performance can be guaranteed via exploiting a new performance function without considering the accurate initial error. Second, by integrating neural network approximation capability into the backstepping technique, a robust adaptive neural control scheme is developed to deal with unknown nonlinear functions, stochastic disturbances and unknown hysteresis input. The designed controller overcomes the problem of the over-parameterization. Under the proposed controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB), and the prespecified transient and steady tracking control performance are guaranteed. Simulation studies are performed to demonstrate and verify the effectiveness of the proposed method.

This paper proposes a novel method for the electrocardiographic (ECG) beat classification via deterministic learning. The dynamics of ECG beats is used as a unique feature for ECG beat classification, which is fundamentally different from the time/frequency domain features used in literature. It is the essential feature of ECG beats, and contains complete information of ECG beats. Precisely, the deterministic learning allows us to model and represent the dynamics of a training beat set as constant radial basis function (RBF) networks. As the classification measure, a set of errors is further obtained through the comparison between the test beat and the estimators constructed by the RBF networks. ECG records taken from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database are selected to test the proposed method. With 5% beats used as training beats, the overall accuracies are 97.78% and 97.21% for global and patient-adapting beat classification, respectively. These results indicate the proposed method is reliable and efficient for ECG beat classification.

This paper presents an adaptive neural tracking control scheme for strict-feedback stochastic nonlinear systems with guaranteed transient and steady-state performance under arbitrary switchings. First, by utilising the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, radial basis function neural networks approximation are used to handle unknown nonlinear functions and stochastic disturbances. At last, by using the common Lyapunov function method and the backstepping technique, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterisation, and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded, and the prescribed tracking control performance are guaranteed under arbitrary switchi...

An adaptive tracking control is investigated for a class of nonstrict-feedback nonlinear systems with time delays subject to input saturation nonlinearity and output constraint. First, the Gaussian error function is used to express the continuous differentiable asymmetric saturation model, and a barrier Lyapunov function is designed to ensure that the output parameters are restricted. Then, an appropriate Lyapunov–Krasovskii functional is chosen to deal with the unknown time-delay terms, and the neural network is used to model the unknown nonlinearities. Finally, based on Lyapunov stability theory, an adaptive neural controller is designed to establish the closed-loop system stability. The example is provided to further illustrate the effectiveness and applicability of the proposed approach.

This paper proposes an adaptive neural outputfeedback controller for a class of nonstrict-feedback stochastic nonlinear time-delay systems with unknown backlash-like hysteresis. First, a state observer is established for estimating the unmeasured states. Second, an appropriate Lyapunov-Krasovskii functional is used to compensate the unknown time-delay terms, and the neural network is employed to approximate the unknown nonlinear functions. At last, an output-feedback adaptive neural control scheme is constructed by using Lyapunov stability theory and backstepping technique. It is shown that the designed neural controller can ensure that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are presented to further show the effectiveness of the proposed approach.

This paper deals with the problems concerned with the trajectory tracking control with prescribed performance for marine surface vessels without velocity measurements in uncertain dynamical environments, in the presence of parametric uncertainties, unknown disturbances, and unknown dead-zone. First, only the ship position and heading measurements are available and a high-gain observer is used to estimate the unmeasurable velocities. Second, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed via the preprocessing. At last, based on neural network approximation in combination with backstepping and Lyapunov synthesis, a robust adaptive neural control scheme is developed to handle the uncertainties and input dead-zone characteristics. Under the designed adaptive controller for marine surface vessels, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the prescribed transient and steady tracking control performance is guaranteed. Simulation studies are performed to demonstrate the effectiveness of the proposed method.

This paper solves the problem of an adaptive neural dynamic surface control for a class of uncertain strict-feedback nonlinear systems with guaranteed transient and steady-state performance under arbitrary switchings. First, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, radial basis function (RBF) neural networks (NNs) are used to handle unknown nonlinear functions, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model and the dynamic surface control (DSC) technique is used to overcome the problem of 'explosion of complexity' inherent in the control design. At last, by using the common Lyapunov function method in combination with the backstepping technology, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterization, and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are bounded, and the prescribed transient and steady tracking control performance are guaranteed under arbitrary switchings. Simulation studies demonstrate the effectiveness of the proposed method.

Statement of problem Colored zirconia is widely used in dental clinical practice; however, data pertaining to its wear resistance after different surface treatments are sparse. Purpose The purpose of this in vitro study was to evaluate the 2-body wear resistance of dental colored zirconia after different glazing and polishing treatments. Material and methods Standardized specimens were prepared from dental zirconia (internal and external staining and no staining) and subjected to different surface treatments. The stained zirconia and control ceramics were polished with a Robinson brush and polishing paste or polishing kits, while the nonstained zirconia was airborne-particle abraded and glazed. The specimens were then abraded against steatite antagonists using a pin-on-disk wear tester. The wear depth for the specimens was measured using confocal microscopy. Wear areas on the steatite antagonists were measured by using an optical microscope. Scanning electron microscopy (SEM) was used to evaluate the wear pattern of the zirconia specimens. All data were statistically analyzed with 1-way ANOVA and the Tamhane test for post hoc analysis (α=.05). Results The surfaces polished using the Robinson brush and paste showed no wear. The wear depth of the unglazed surfaces was 42.27 ±3.21 ∼84.15 ±2.57 μm and 87.75 ±9.36 and 91.76 ±13.58 μm for the glazed surfaces. The antagonist wear area was 1.79 ±0.21 ∼2.69 ±0.34 mm 2 (unglazed) and 3.34 ±0.29 ∼4.51 ±0.88 mm 2 (glazed). SEM revealed chipping fractures, and peeling cracks were observed on the glazed zirconia surfaces, indicating a combination of fatigue and abrasive wear. Conclusions The results of this in vitro study suggest that highly polished zirconia shows the least wear, including antagonist wear. Furthermore, glazed zirconia can be significantly more abrasive than polished zirconia. The wear properties of internally and externally stained zirconia are similar.

Defective TiO2 has attracted increasing attention for use in photocatalytic and electrochemical materials because of its narrowed band-gap and improved visible-light photocatalytic activity. However, a facile and efficient approach for obtaining defect-rich TiO2 still remains a challenge. Herein, we demonstrate such an approach to narrow its bandgap and improve visible-light absorption through implanting abundant defects by aerodynamic levitated laser annealing (ALLA) treatment. Note that the ALLA method not only provides rapid annealing, solidifying and cooling process, but also exhibits high efficiency for homogeneous and defective TiO2 nanoparticles. The laser-annealed TiO2 achieves a high hydrogen evolution rate of 8.54 mmol·h–1·g–1, excellent decomposition properties within 60 min, and outstanding recyclability and stability, all of which are superior to the corresponding properties of commercial P25.

A terminal guidance law basing on variable universe direct adaptive fuzzy approach is developed for intercepting maneuvering space targets. By defining the target acceleration as an unknown disturbance, the adaptive laws on a Lyapunov function are developed to adjust the parameters of the fuzzy rules to guarantee the stability of the system. The guidance law not only keeps the angle rate of line of sight between interception and target converging to zero, but also guarantees intercepting all kinds of maneuvering targets. The simulation results show that by introducing the guidance law will achieve the almost zero miss distance and the robustness.

In this paper, the problem of decentralized adaptive neural backstepping control is investigated for high-order stochastic nonlinear systems with unknown interconnected nonlinearity and prescribed performance under arbitrary switchings. For the control of high-order nonlinear interconnected systems, it is assumed that unknown system dynamics and arbitrary switching signals are unknown. First, by utilizing the prescribed performance control (PPC), the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, at each recursive step, only one adaptive parameter is constructed to overcome the over-parameterization, and RBF neural networks are employed to tackle the difficulties caused by completely unknown system dynamics. At last, based on the common Lyapunov stability method, the decentralized adaptive neural control method is proposed, which decreases the number of learning parameters. It is shown that the designed common controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the prescribed tracking control performance is guaranteed under arbitrary switchings. The simulation results are presented to further illustrate the effectiveness of the proposed control scheme.

This paper deals with the sensorless direct torque control (DTC) scheme for the permanent magnet synchronous motor (PMSM), where a new estimation algorithm is developed based on the polynomial chaos theory and the extended Kalman filter (EKF) for the sensorless obtaining of the rotor speed, stator flux and electromagnetic torque. The proposed estimator can effectively deal with parametric uncertainties and hence reduce the excessive dependence on the PMSM model of the sensorless DTC control. Additionally, it offers an improved estimation performance in contrast to the traditional EKF. Computational results are also provided to illustrate the feasibility of the proposed method.

In the paper, a novel method for identification of distributed parameter system (DPS) with recurrent trajectory via deterministic learning is proposed. The system dynamics rather than the system parameters or system structure is identified, which is completely different from the existing literature. The DPS, which is often described by partial differential equation (PDE), is first transformed into a set of ordinary differential equations (ODEs) by using a special case of finite difference method, method of lines. Then, the system dynamics at each discrete node is identified by using deterministic learning. Without loss of generality, the DPS dynamics at any spatial point in the spatial domain Ω can also be accurately identified by using the proposed method. It will be more appropriate for DPS identification in many physical systems whose both structure and parameters are unknown. Simulation results have shown the feasibility and efficient of the proposed method.

This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method.

Objectives The purpose of this study is to elucidate the effects of small-grit grinding on the mechanical behaviors and ageing resistance of a super-translucent dental zirconia and to investigate the necessity of glazing for the small-grit ground zirconia. Methods Small-grit grinding was performed using two kinds of silicon carbide abrasive papers. The control group received no grinding. The unground surfaces and the ground surfaces were glazed by an experienced dental technician. Finally, the zirconia materials were thermally aged in water at 134 °C for 5 h. After aforementioned treatments, we observed the surface topography and the microstructures, and measured the extent of monoclinic phase, the nano-hardness and nano-modulus of the possible transformed zone and the flexural strength. Results Small-grit grinding changed the surface topography. The zirconia microstructure did not change obviously after surface treatments and thermal ageing; however, the glaze in contact with zirconia showed cracks after thermal ageing. Small-grit grinding did not induce a phase transformation but improved the flexural strength and ageing resistance. Glazing prevented zirconia from thermal ageing but severely diminished the flexural strength. The nano-hardness and nano-modulus of the surface layer were increased by ultrafine grinding. Conclusions The results suggest that small-grit grinding is beneficial to the strength and ageing resistance of the super-translucent dental zirconia; however, glazing is not necessary and even impairs the strength for the super-translucent dental zirconia. Clinical significance This study is helpful to the researches about dental grinding tools and maybe useful for dentists to choose reasonable zirconia surface treatments.